{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\linearmodels\\panel\\data.py:10: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import (Categorical, DataFrame, Index, MultiIndex, Panel, Series,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd             # data package\n",
    "import matplotlib.pyplot as plt # graphics \n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "import requests, io             # internet and input tools  \n",
    "import zipfile as zf            # zip file tools \n",
    "import os  \n",
    "\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels\n",
    "#import statsmodels.formula.api as smf\n",
    "from linearmodels.iv import IV2SLS\n",
    "from linearmodels.panel import PanelOLS\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig_path = \"C:\\\\github\\\\expenditure_tradeshocks\\\\figures\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: driver_flag\n",
    "    \n",
    "except NameError: driver_flag = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = os.getcwd()\n",
    "\n",
    "fig_path = file_path +\"\\\\figures\"\n",
    "\n",
    "trade_county = pq.read_table(file_path + \"\\\\data\\\\trade_employment_blssingle19.parquet\").to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_county[\"time\"] = pd.to_datetime(trade_county.time)\n",
    "\n",
    "trade_county.set_index([\"area_fips\", \"time\"],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_county[\"log_tariff\"] = np.log(1+.01*trade_county[\"tariff\"])\n",
    "\n",
    "trade_county[\"log_exp_total\"] = np.log(trade_county[\"total_exp_pc\"]).replace(-np.inf, np.nan)\n",
    "\n",
    "trade_county[\"log_exp_china\"] = np.log(trade_county[\"china_exp_pc\"]).replace(-np.inf, np.nan)\n",
    "\n",
    "#emp_measure = \"emp_all\"\n",
    "# Here you specify the type of employment you want. emp_all is total employment. emp_gds = goods employment. \n",
    "\n",
    "trade_county[\"log_employment\"] = np.log(trade_county[emp_measure]).replace(-np.inf, np.nan)\n",
    "# Now given how the file is setup, we can select which type of employment.\n",
    "# all private, goods, or manufacturing.\n",
    "\n",
    "#trade_county[\"log_employment\"] = np.log(trade_county[\"emp_all\"] - \n",
    "                                        #trade_county[\"emp_gds\"]).replace(-np.inf, np.nan)\n",
    "\n",
    "# Here is the issue, if you just subtract stuff, there are some missing observations relative\n",
    "# to the direct look at non-goods employment ('use emp_n_gds'). So the later is appears \n",
    "# better, though you can find instances where this approach above delivers values, compramise\n",
    "# would be to ``fill in'' things...safest is always just look at total employment; any break\n",
    "# down starts to get into these issues.\n",
    "\n",
    "trade_county[\"const\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_county.reset_index(inplace = True)\n",
    "\n",
    "trade_county.rename({\"area_fips\": \"GEOFIPS\"},axis = 1, inplace = True)\n",
    "\n",
    "trade_county[\"state_fips\"] = trade_county[\"GEOFIPS\"].astype(str).str[0:2]\n",
    "\n",
    "trade_county[\"GEOFIPS\"] = trade_county[\"GEOFIPS\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_county.set_index([\"GEOFIPS\", \"time\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_exp_pc</th>\n",
       "      <th>china_exp_pc</th>\n",
       "      <th>tariff</th>\n",
       "      <th>emplvl_2017</th>\n",
       "      <th>fips</th>\n",
       "      <th>total_employment</th>\n",
       "      <th>emp_rtl</th>\n",
       "      <th>emp_all</th>\n",
       "      <th>emp_gds</th>\n",
       "      <th>emp_ngds</th>\n",
       "      <th>rural_share</th>\n",
       "      <th>2010_population</th>\n",
       "      <th>2017_income</th>\n",
       "      <th>2017_population</th>\n",
       "      <th>log_tariff</th>\n",
       "      <th>log_exp_total</th>\n",
       "      <th>log_exp_china</th>\n",
       "      <th>log_employment</th>\n",
       "      <th>const</th>\n",
       "      <th>state_fips</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOFIPS</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">10001</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>453.257185</td>\n",
       "      <td>47.280196</td>\n",
       "      <td>1.069532</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>10001</td>\n",
       "      <td>29514.0</td>\n",
       "      <td>9269.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38494.0</td>\n",
       "      <td>0.269694</td>\n",
       "      <td>162310.0</td>\n",
       "      <td>57647.0</td>\n",
       "      <td>173145.0</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>6.116460</td>\n",
       "      <td>3.856092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>471.930726</td>\n",
       "      <td>47.211522</td>\n",
       "      <td>1.069499</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>10001</td>\n",
       "      <td>29514.0</td>\n",
       "      <td>9236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38646.0</td>\n",
       "      <td>0.269694</td>\n",
       "      <td>162310.0</td>\n",
       "      <td>57647.0</td>\n",
       "      <td>173145.0</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>6.156832</td>\n",
       "      <td>3.854638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>485.376760</td>\n",
       "      <td>35.078484</td>\n",
       "      <td>1.069500</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>10001</td>\n",
       "      <td>29514.0</td>\n",
       "      <td>9342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38917.0</td>\n",
       "      <td>0.269694</td>\n",
       "      <td>162310.0</td>\n",
       "      <td>57647.0</td>\n",
       "      <td>173145.0</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>6.184925</td>\n",
       "      <td>3.557588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>460.259354</td>\n",
       "      <td>27.991526</td>\n",
       "      <td>1.069500</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>10001</td>\n",
       "      <td>29514.0</td>\n",
       "      <td>9376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39719.0</td>\n",
       "      <td>0.269694</td>\n",
       "      <td>162310.0</td>\n",
       "      <td>57647.0</td>\n",
       "      <td>173145.0</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>6.131790</td>\n",
       "      <td>3.331902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>473.572638</td>\n",
       "      <td>28.235163</td>\n",
       "      <td>1.069499</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>10001</td>\n",
       "      <td>29514.0</td>\n",
       "      <td>9265.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40164.0</td>\n",
       "      <td>0.269694</td>\n",
       "      <td>162310.0</td>\n",
       "      <td>57647.0</td>\n",
       "      <td>173145.0</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>6.160305</td>\n",
       "      <td>3.340568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_exp_pc  china_exp_pc    tariff  emplvl_2017   fips  \\\n",
       "GEOFIPS time                                                                   \n",
       "10001   2016-01-01    453.257185     47.280196  1.069532       2843.0  10001   \n",
       "        2016-02-01    471.930726     47.211522  1.069499       2843.0  10001   \n",
       "        2016-03-01    485.376760     35.078484  1.069500       2843.0  10001   \n",
       "        2016-04-01    460.259354     27.991526  1.069500       2843.0  10001   \n",
       "        2016-05-01    473.572638     28.235163  1.069499       2843.0  10001   \n",
       "\n",
       "                    total_employment  emp_rtl  emp_all  emp_gds  emp_ngds  \\\n",
       "GEOFIPS time                                                                \n",
       "10001   2016-01-01           29514.0   9269.0      0.0      0.0   38494.0   \n",
       "        2016-02-01           29514.0   9236.0      0.0      0.0   38646.0   \n",
       "        2016-03-01           29514.0   9342.0      0.0      0.0   38917.0   \n",
       "        2016-04-01           29514.0   9376.0      0.0      0.0   39719.0   \n",
       "        2016-05-01           29514.0   9265.0      0.0      0.0   40164.0   \n",
       "\n",
       "                    rural_share  2010_population  2017_income  \\\n",
       "GEOFIPS time                                                    \n",
       "10001   2016-01-01     0.269694         162310.0      57647.0   \n",
       "        2016-02-01     0.269694         162310.0      57647.0   \n",
       "        2016-03-01     0.269694         162310.0      57647.0   \n",
       "        2016-04-01     0.269694         162310.0      57647.0   \n",
       "        2016-05-01     0.269694         162310.0      57647.0   \n",
       "\n",
       "                    2017_population  log_tariff  log_exp_total  log_exp_china  \\\n",
       "GEOFIPS time                                                                    \n",
       "10001   2016-01-01         173145.0    0.010639       6.116460       3.856092   \n",
       "        2016-02-01         173145.0    0.010638       6.156832       3.854638   \n",
       "        2016-03-01         173145.0    0.010638       6.184925       3.557588   \n",
       "        2016-04-01         173145.0    0.010638       6.131790       3.331902   \n",
       "        2016-05-01         173145.0    0.010638       6.160305       3.340568   \n",
       "\n",
       "                    log_employment  const state_fips  \n",
       "GEOFIPS time                                          \n",
       "10001   2016-01-01             NaN      1         10  \n",
       "        2016-02-01             NaN      1         10  \n",
       "        2016-03-01             NaN      1         10  \n",
       "        2016-04-01             NaN      1         10  \n",
       "        2016-05-01             NaN      1         10  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_county.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First take growth rates\n",
    "\n",
    "# note , I'm a bit confused about why the sorting,\n",
    "# here is that because of some missing values, the resulting dateframe from the \n",
    "# first operation may be out of place, so we need to resort things to make sure that\n",
    "# the time difference is correct.\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)\n",
    "\n",
    "trade_county[\"tariff_change\"] = trade_county.groupby([\"GEOFIPS\"]).tariff.diff(12)\n",
    "\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)\n",
    "\n",
    "trade_county[\"log_tariff_change\"] = trade_county.groupby([\"GEOFIPS\"]).log_tariff.diff(12)\n",
    "\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)\n",
    "\n",
    "trade_county[\"employment_growth\"] = trade_county.groupby([\"GEOFIPS\"]).log_employment.diff(12)\n",
    "\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)\n",
    "\n",
    "trade_county[\"total_trade_growth\"] = trade_county.groupby([\"GEOFIPS\"]).log_exp_total.diff(12)\n",
    "\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)\n",
    "\n",
    "trade_county[\"china_trade_growth\"] = trade_county.groupby([\"GEOFIPS\"]).log_exp_china.diff(12)\n",
    "\n",
    "trade_county.sort_values([\"GEOFIPS\", \"time\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = lambda df: df.iloc[-1]\n",
    "\n",
    "#trade_county.groupby([\"GEOFIPS\"]).log_tariff.transform(trans)\n",
    "\n",
    "trade_county[\"lead_tariff\"] = trade_county.groupby([\"GEOFIPS\"]).log_tariff_change.transform(trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_county[\"man_share\"] = trade_county[\"emp_gds\"] / trade_county[\"emp_all\"]\n",
    "\n",
    "trade_county[\"man_share\"] = trade_county[\"man_share\"].replace(np.inf, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = lambda df: df.iloc[0:11].mean()\n",
    "\n",
    "#trade_county.groupby([\"GEOFIPS\"]).log_tariff.transform(trans)\n",
    "\n",
    "trade_county[\"avg_man_share\"] = trade_county.groupby([\"GEOFIPS\"]).man_share.transform(trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27251695676746807"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_county[\"avg_man_share\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M[1, 2]_17\n",
      "1\n",
      "M[3, 4]_17\n",
      "2\n",
      "M[5, 6]_17\n",
      "3\n",
      "M[7, 8]_17\n",
      "4\n",
      "M[9, 10]_17\n",
      "5\n",
      "M[11, 12]_17\n",
      "6\n",
      "M[1, 2]_18\n",
      "7\n",
      "M[3, 4]_18\n",
      "8\n",
      "M[5, 6]_18\n",
      "9\n",
      "M[7, 8]_18\n",
      "10\n",
      "M[9, 10]_18\n",
      "11\n",
      "M[11, 12]_18\n",
      "12\n",
      "M[1, 2]_19\n",
      "13\n",
      "M[3, 4]_19\n",
      "14\n",
      "M[5, 6]_19\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "years = [17,18,19]\n",
    "\n",
    "quarters = [[1,2],[3,4],[5,6],[7,8],[9,10],[11,12]]\n",
    "\n",
    "#quarters = [[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12]]\n",
    "\n",
    "rural_list = []\n",
    "\n",
    "man_list = []\n",
    "\n",
    "tariff_list = []\n",
    "\n",
    "count = 0\n",
    "\n",
    "event_study = pd.DataFrame([])\n",
    "\n",
    "for xxx in years:\n",
    "    \n",
    "    for yyy in quarters:\n",
    "        \n",
    "        if (xxx == 19) & (yyy[0] > 6):\n",
    "            break\n",
    "            \n",
    "        \n",
    "        year = \"20\" + str(xxx)\n",
    "        year = int(year)\n",
    "        \n",
    "        date_cond = ((trade_county.index.get_level_values(1).month.isin(yyy)) \n",
    "             & (trade_county.index.get_level_values(1).year == year ))\n",
    "        \n",
    "        name = \"M\" + str(yyy) + \"_\" + str(xxx)\n",
    "        \n",
    "        #print(name)\n",
    "        \n",
    "        if count == 0:\n",
    "            \n",
    "            event_study = pd.merge(trade_county.reset_index(),\n",
    "                                   pd.get_dummies(date_cond)[1].rename(name),\n",
    "                                   left_index = True, right_index= True)\n",
    "        else:\n",
    "            \n",
    "            event_study = pd.merge(event_study, \n",
    "                       pd.get_dummies(date_cond)[1].rename(name),\n",
    "                                   left_index = True, right_index= True)\n",
    "               \n",
    "        name_t = name + \"_t\"\n",
    "        \n",
    "        name_r = name + \"_r\"\n",
    "        \n",
    "        name_m = name + \"_m\"\n",
    "        \n",
    "        event_study[name_t] = event_study.lead_tariff.multiply(event_study[name], axis = 0)\n",
    "        # This will be the dummy variable on the leads\n",
    "\n",
    "        event_study[name_r] = event_study.rural_share.multiply(event_study[name], axis = 0)\n",
    "         # This will be the dummy variable on ineraction with rural share\n",
    "\n",
    "        event_study[name_m] = event_study.avg_man_share.multiply(event_study[name], axis = 0) \n",
    "        # This will be the dummy variable on ineraction with goods employment share\n",
    "        \n",
    "        count = count + 1\n",
    "        \n",
    "        tariff_list.append(name_t)\n",
    "        \n",
    "        man_list.append(name_m)\n",
    "        \n",
    "        rural_list.append(name_r)\n",
    "        \n",
    "        #print(count)\n",
    "        \n",
    "######################################################## \n",
    "        \n",
    "event_study.set_index([\"GEOFIPS\", \"time\"],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97560, 1)\n",
      "(97560,)\n",
      "30\n",
      "3252\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:      employment_growth   R-squared:                        0.0243\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0420\n",
      "No. Observations:               93624   R-squared (Within):               0.0045\n",
      "Date:                Mon, Dec 09 2019   R-squared (Overall):              0.0220\n",
      "Time:                        11:59:40   Log-likelihood                 2.174e+05\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      804.66\n",
      "Entities:                        3129   P-value                           0.0000\n",
      "Avg Obs:                       29.921   Distribution:                F(45,93549)\n",
      "Min Obs:                       3.0000                                           \n",
      "Max Obs:                       30.000   F-statistic (robust):             6.2362\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      30   Distribution:                F(45,93549)\n",
      "Avg Obs:                       3120.8                                           \n",
      "Min Obs:                       3119.0                                           \n",
      "Max Obs:                       3124.0                                           \n",
      "                                                                                \n",
      "                               Parameter Estimates                                \n",
      "==================================================================================\n",
      "                Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "----------------------------------------------------------------------------------\n",
      "const              0.0172     0.0013     13.258     0.0000      0.0147      0.0197\n",
      "M[1, 2]_17_t      -0.1302     0.0893    -1.4573     0.1450     -0.3053      0.0449\n",
      "M[3, 4]_17_t      -0.0821     0.0818    -1.0039     0.3154     -0.2425      0.0782\n",
      "M[5, 6]_17_t      -0.1721     0.0934    -1.8429     0.0653     -0.3550      0.0109\n",
      "M[7, 8]_17_t       0.0183     0.0938     0.1948     0.8455     -0.1655      0.2021\n",
      "M[9, 10]_17_t     -0.0103     0.0945    -0.1092     0.9130     -0.1955      0.1748\n",
      "M[11, 12]_17_t     0.0515     0.1001     0.5140     0.6072     -0.1447      0.2476\n",
      "M[1, 2]_18_t       0.1318     0.1147     1.1486     0.2507     -0.0931      0.3566\n",
      "M[3, 4]_18_t      -0.1015     0.0985    -1.0305     0.3028     -0.2944      0.0915\n",
      "M[5, 6]_18_t      -0.0970     0.0922    -1.0520     0.2928     -0.2777      0.0837\n",
      "M[7, 8]_18_t      -0.2534     0.0858    -2.9544     0.0031     -0.4215     -0.0853\n",
      "M[9, 10]_18_t     -0.3231     0.0792    -4.0812     0.0000     -0.4783     -0.1679\n",
      "M[11, 12]_18_t    -0.2594     0.0791    -3.2804     0.0010     -0.4144     -0.1044\n",
      "M[1, 2]_19_t      -0.2890     0.0776    -3.7224     0.0002     -0.4411     -0.1368\n",
      "M[3, 4]_19_t      -0.2415     0.0784    -3.0785     0.0021     -0.3952     -0.0877\n",
      "M[5, 6]_19_t      -0.1500     0.0758    -1.9793     0.0478     -0.2985     -0.0015\n",
      "M[1, 2]_17_m      -0.0309     0.0104    -2.9776     0.0029     -0.0513     -0.0106\n",
      "M[3, 4]_17_m      -0.0135     0.0098    -1.3801     0.1676     -0.0326      0.0057\n",
      "M[5, 6]_17_m      -0.0020     0.0093    -0.2156     0.8293     -0.0203      0.0163\n",
      "M[7, 8]_17_m      -0.0053     0.0096    -0.5568     0.5776     -0.0242      0.0135\n",
      "M[9, 10]_17_m      0.0018     0.0101     0.1783     0.8585     -0.0180      0.0217\n",
      "M[11, 12]_17_m     0.0081     0.0101     0.8016     0.4228     -0.0117      0.0278\n",
      "M[1, 2]_18_m       0.0182     0.0114     1.5899     0.1119     -0.0042      0.0406\n",
      "M[3, 4]_18_m       0.0228     0.0116     1.9684     0.0490   9.767e-05      0.0454\n",
      "M[5, 6]_18_m       0.0231     0.0097     2.3796     0.0173      0.0041      0.0421\n",
      "M[7, 8]_18_m       0.0300     0.0089     3.3781     0.0007      0.0126      0.0475\n",
      "M[9, 10]_18_m      0.0349     0.0087     4.0334     0.0001      0.0179      0.0519\n",
      "M[11, 12]_18_m     0.0291     0.0087     3.3240     0.0009      0.0119      0.0462\n",
      "M[1, 2]_19_m       0.0144     0.0083     1.7385     0.0821     -0.0018      0.0307\n",
      "M[3, 4]_19_m       0.0065     0.0080     0.8128     0.4163     -0.0092      0.0223\n",
      "M[5, 6]_19_m       0.0024     0.0081     0.2977     0.7659     -0.0134      0.0182\n",
      "M[1, 2]_17_r      -0.0087     0.0033    -2.6421     0.0082     -0.0151     -0.0022\n",
      "M[3, 4]_17_r      -0.0125     0.0030    -4.2115     0.0000     -0.0183     -0.0067\n",
      "M[5, 6]_17_r      -0.0150     0.0027    -5.5433     0.0000     -0.0203     -0.0097\n",
      "M[7, 8]_17_r      -0.0145     0.0026    -5.5652     0.0000     -0.0196     -0.0094\n",
      "M[9, 10]_17_r     -0.0137     0.0026    -5.2987     0.0000     -0.0187     -0.0086\n",
      "M[11, 12]_17_r    -0.0159     0.0026    -6.2230     0.0000     -0.0209     -0.0109\n",
      "M[1, 2]_18_r      -0.0213     0.0029    -7.3292     0.0000     -0.0270     -0.0156\n",
      "M[3, 4]_18_r      -0.0182     0.0028    -6.5145     0.0000     -0.0236     -0.0127\n",
      "M[5, 6]_18_r      -0.0131     0.0026    -5.0959     0.0000     -0.0181     -0.0080\n",
      "M[7, 8]_18_r      -0.0142     0.0025    -5.7108     0.0000     -0.0191     -0.0093\n",
      "M[9, 10]_18_r     -0.0161     0.0025    -6.4011     0.0000     -0.0210     -0.0112\n",
      "M[11, 12]_18_r    -0.0138     0.0025    -5.4704     0.0000     -0.0187     -0.0089\n",
      "M[1, 2]_19_r      -0.0093     0.0023    -4.0518     0.0001     -0.0139     -0.0048\n",
      "M[3, 4]_19_r      -0.0106     0.0021    -4.9606     0.0000     -0.0149     -0.0064\n",
      "M[5, 6]_19_r      -0.0138     0.0021    -6.4643     0.0000     -0.0180     -0.0096\n",
      "==================================================================================\n",
      "\n",
      "F-test for Poolability: 10.284\n",
      "P-value: 0.0000\n",
      "Distribution: F(29,93549)\n",
      "\n",
      "Included effects: Time\n"
     ]
    }
   ],
   "source": [
    "all_vars = [\"const\", \"log_tariff_change\", 'employment_growth', \"total_employment\", \"total_trade_growth\",\n",
    "           \"avg_man_share\", \"rural_share\", \"2010_population\"]\n",
    "\n",
    "all_vars.extend(tariff_list)\n",
    "\n",
    "all_vars.extend(man_list)\n",
    "\n",
    "all_vars.extend(rural_list)\n",
    "            \n",
    "idx = pd.IndexSlice\n",
    "\n",
    "#weights = trade_county[\"emplvl_2017\"].loc[idx[:,\"2018-01-01\":\"2019-02-01\"]]\n",
    "\n",
    "dataset = event_study[all_vars].loc[idx[:,\"2017-01-01\":\"2019-6-01\"],:]\n",
    "\n",
    "exog_vars = [\"const\"]\n",
    "\n",
    "exog_vars.extend(tariff_list)\n",
    "\n",
    "exog_vars.extend(man_list)\n",
    "\n",
    "exog_vars.extend(rural_list)\n",
    "\n",
    "weights = dataset[\"2010_population\"].to_frame()\n",
    "\n",
    "weights.replace(to_replace = 0, value = 0.001,inplace = True)\n",
    "\n",
    "print(weights.shape)\n",
    "print(dataset.employment_growth.shape)\n",
    "\n",
    "mod = PanelOLS(dataset.employment_growth, dataset[exog_vars], weights = weights, time_effects = True)\n",
    "\n",
    "fe_res = mod.fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "if driver_flag != True:\n",
    "\n",
    "    print(fe_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test2 = fe_res.conf_int(level=0.90).iloc[1:16].merge(fe_res.params, left_index = True, right_index = True)\n",
    "\n",
    "start = dt.datetime(2017,1,1)\n",
    "end = dt.datetime(2019,7,1)\n",
    "\n",
    "test2.index = pd.date_range(start=start, end=end, freq = \"2M\")\n",
    "\n",
    "#########################################################################\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (12,8))\n",
    "\n",
    "mike_blue = tuple(np.array([20, 64, 134]) / 255)\n",
    "\n",
    "ax.plot(test2.parameter, alpha = 0.95, color = mike_blue, linewidth = 2, linestyle= \"--\",\n",
    "       marker='o', markersize = 8)\n",
    "\n",
    "ax.fill_between(test2.index, test2.lower, test2.upper, color = \"#3F5D7D\", alpha = 0.1)\n",
    "\n",
    "ax.set_ylabel('Estimates of Lead Tariff Effect', fontsize = 14)\n",
    "\n",
    "ax.set_title(\"Estimates of Lead Tariff on Total Employment\", fontsize = 16, loc= \"left\" )\n",
    "\n",
    "ax.hlines(0, start, end,\n",
    "           linewidth = 2, color = 'darkred', alpha =0.75, linestyle = \"--\")\n",
    "\n",
    "#ax.set_ylim(-0.5,0.40)\n",
    "\n",
    "ax.set_xlim(test2.index[0]- timedelta(4),test2.index[-1] + timedelta(10))\n",
    "\n",
    "months = mdates.MonthLocator((12,3,6,9))\n",
    "ax.xaxis.set_major_locator(months)\n",
    "\n",
    "ax.yaxis.grid(alpha= 0.5, linestyle= \"--\")\n",
    "\n",
    "ax.axvline(dt.datetime(2018,4,4), linewidth = 2, ls = \"--\", color = 'k', alpha =0.15)\n",
    "\n",
    "ax.axvline(dt.datetime(2018,7,6), linewidth = 2, ls = \"--\", color = 'k', alpha =0.15)\n",
    "\n",
    "ax.axvline(dt.datetime(2018,8,23), linewidth = 2, ls = \"--\", color = 'k', alpha =0.15)\n",
    "\n",
    "ax.axvline(dt.datetime(2018,9,24), linewidth = 2, ls = \"--\", color = 'k', alpha =0.15)\n",
    "\n",
    "ax.axvline(dt.datetime(2018,12,1), linewidth = 2, ls = \"--\", color = 'k', alpha =0.15)\n",
    "\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "emp_type = emp_measure.rsplit(\"_\")[1]\n",
    "\n",
    "if not os.path.exists(fig_path):\n",
    "    os.makedirs(fig_path)\n",
    "\n",
    "plt.savefig(fig_path + \"\\\\employment_\" + emp_type + \"_pretrend.pdf\", bbox_inches = \"tight\", dip = 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-1eb00ff78cf2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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